Abhisek Chakraborty

Validation of ocean surface winds from the OCEANSAT-2 scatterometer using triple collocation

Ocean surface winds from the OCEANSAT-2 scatterometer (OSCAT) were validated with equivalent neutral wind observations from 87 global buoys and winds from the European Centre for Medium Range Weather Forecasting (ECMWF) Numerical Weather Prediction (NWP) model using triple collocation for a period of 9 months. Functional relationship analysis (FRA) employing the error-in-variables method is found to be more ‘exact’ in comparison with classical linear regression analysis for the validation of the OSCAT data. Moreover, using the wind component domain for validation and error assessment rather than the speed and direction domain is confirmed to be favourable. The FRA method applied on the triple-collocated wind components shows that the error standard deviations of the OSCAT and buoy winds are quite similar. The calibration trends and biases for OSCAT, buoys and ECMWF are found to be close to unity and zero, respectively.

A SEEK filter assimilation of sea surface salinity from Aquarius in an OGCM: Implication for surface dynamics and thermohaline structure

Singular Evolutive Extended Kalman (SEEK) filter has been used to assimilate Aquarius-derived sea surface salinity (SSS) in a near-global ocean general circulation model (OGCM). Advanced Very High Resolution Radiometer (AVHRR)-derived sea surface temperature (SST) has also been assimilated in conjunction. The primary aim of the study is to investigate the improvement in simulation of global ocean surface currents as a result of this assimilation. The route of empirical orthogonal function (EOF) analysis has been taken for an efficient assessment of this impact separately in the space and time domains and satellite-derived surface current has been used as a benchmark. As expected, the assimilation has been found to impart significant positive impact in both the domains. Also, joint assimilation of SSS and SST has been found to be better than standalone SSS assimilation. These results have been further corroborated by a comparison with buoy-derived surface currents. Further emphasis has been laid on the simulation of Wyrtki and monsoon jets in the equatorial Indian Ocean, because of their importance in the climate of this region and again it has been found that assimilation guides the simulation toward realism in both the cases. Finally, impact on the SSS and SST fronts and their zonal displacements in the western Pacific has been investigated and here again the assimilation has led to an improvement in simulation of these features.

Generation and validation of analysed wind vectors over the global oceans

Daily and 12 hourly gridded analysed wind vectors (AWVs) over global ocean were generated using the simple spatial interpolation scheme of ‘box averaging’, with a horizontal resolution of 0.5° × 0.5°. For daily analysed winds, observations only from Oceansat-2 Scatterometer (OSCAT) were used. The 12 hourly AWVs were generated by combining the data from both OSCAT and Advanced Scatterometer (ASCAT). Apart from ocean wind vectors, an effort was made also to produce analysed wind stress, divergence and curl of wind stress. The daily and 12 hourly analysed winds were validated using in situ observations from 97 global moored buoys and data from European Centre for Medium Range Weather Forecasting analyses for a period of 9 months. The validation result shows a good agreement between AWV products and the buoy and model analysis data, yielding a standard deviation of around 2 m s−1 in wind speed and around 20° in wind direction.

Intercomparison of OSCAT Winds With Numerical-Model-Generated Winds

Subsequent to the launch of an ocean scatterometer onboard the Oceansat-2 satellite, hereby referred to as OSCAT, on September 23, 2009, the nine-month period from November 2009 to July 2010 was the validation phase for the retrieved ocean surface winds. This letter focuses on one section of the validation campaign, where the validation study for OSCAT winds with European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) analyses is carried out. It is found that, in the 4-24-m/s range of wind speed, the rms error in OSCAT wind speed with ECMWF analysis is 1.4 m/s while that with NCEP analysis is 1.7 m/s. In the case of retrieved wind direction, the rms error is 17.2° with ECMWF analyses, while that for NCEP is 18.8°. These statistics are well within the mission goal of 2-m/s accuracy in wind speed and 20 ° in wind directions. This letter discusses the comparison results for different geographical regions and wind speed ranges.

Comparison of oceanic winds measured by space-borne scatterometers and altimeters

Global ocean surface wind speeds from Quick Scatterometer (QuikSCAT) for 5 years (2005–2009) and from Oceansat-2 Scatterometer (OSCAT) for 1 year (2010) were compared with wind speeds estimated from JASON Altimeters for representative months to investigate the consistency in wind speeds between these sensors. The comparison was carried out through statistical analyses. The spatial window used for comparison with JASON was 0.25° for QuikSCAT and 0.5° for OSCAT, while the temporal window for both QuikSCAT and OSCAT was ±30 min. The results of the inter-comparison indicate that OSCAT wind speeds are almost as consistent with JASON as QuikSCAT wind speeds.

Improving Ocean State by Assimilating SARAL/AltiKa Derived Sea Level and Other Satellite-Derived Data in MITGCM

Assimilation of satellite-derived surface datasets has been explored in the study. Three types of surface data, namely sea level anomaly, sea surface temperature and sea surface salinity, have been used in various data assimilation experiments. The emphasis has been on the extra benefit arising out of the additional sea level assimilation and hence there are two parallel runs, in one of which sea level assimilation has been withheld. The model used is a state-of-the art ocean general circulation model (OGCM) and the assimilation method is the widely used singular evolutive extended Kalman filter (SEEK). Evaluation of the assimilation skill has been carried out by comparing the simulated depth of the 20°C isotherm with the same quantity measured by buoys and Argo floats. Simulated subsurface temperature and salinity profiles have also been compared with the same profiles measured by Argo floats. Finally, surface currents in the assimilation runs have been compared with currents measured by several off-equatorial buoys. Addition of sea level has been found to substantially improve the quality of simulation. An important feature that has been effectively simulated by the addition of sea level in the assimilation scheme is the near-surface temperature inversion (2-3°C) in the northern Bay of Bengal.

Response of simulated sea surface temperature and sea level to satellite-derived precipitation

An ocean general circulation model has been used to investigate the response of simulated sea surface temperature (SST) and sea level to satellite-derived precipitation. For this, the precipitation forcing of the control run, obtained from the National Center for Environmental Prediction (NCEP), has been replaced by precipitation derived by the Special Sensor Microwave Imager. The simulations have been compared with observations from tropical moored buoys and satellites, and the comparison statistics clearly shows that the satellite product outperforms the NCEP product. Although the impact of rainfall on SST is significant, there are regions where the SST is rather insensitive to the rainfall. In those regions advective effects come into play for cancelling the rainfall impact. Moreover, the impact on sea level is not as marginal as seems to have been suggested earlier. Finally, rainfall has been found to leave its signature on surface currents also.

Joint assimilation of Aquarius-derived sea surface salinity and AVHRR-derived sea surface temperature in an ocean general circulation model using SEEK filter: Implication for mixed layer depth and barrier layer thickness

Sea surface salinity (SSS) from Aquarius mission and sea surface temperature (SST) from Advanced Very High Resolution Radiometer (AVHRR) for the years 2012–2014 are assimilated into the global Massachusetts Institute of Technology General Circulation Model (MITGCM). Investigation of the impact of assimilation of these two data sets on simulated mixed layer depth (MLD) and barrier layer thickness (BLT) forms the core of our study. The method of assimilation is the Singular Evolutive Extended Kalman (SEEK) filter. Several assimilation runs are performed. Single-parameter assimilation, as well as joint assimilation, is conducted. To begin with, the model simulated SST and SSS are compared with independent Argo observations of these two parameters. Use of latitudinally varying error variances, which is a novel feature of our study, gives rise to the significant improvement in the simulation of SSS and SST. The best result occurs when joint assimilation is performed. Afterward, simulated MLD and BLT are compared with the same parameters derived from Argo observations forming an independent validation data set. Comparisons are performed both in temporal and spatial domains. Significant positive impact of assimilation is found in all the cases studied, and joint assimilation is found to outperform single-parameter assimilation in each of the cases considered. It is found that simulations of MLD and BLT improve up to 24% and 29%, respectively, when a joint assimilation of SSS and SST is carried out.

Validation of wind speed retrieval from RISAT-1 SAR images of the North Indian Ocean

ABSTRACT An algorithm has been developed to retrieve ocean surface wind speed from Synthetic Aperture Radar (SAR) on-board Radar Imaging Satellite-1 (RISAT-1). The retrieved wind speed is subsequently validated using observations from Advanced Scatterometers (on-board Metop-A, Metop-B) for the period from July 2012 to September 2016. The quality of the retrieved wind speed was assessed using observations from offshore moored buoys in the Bay of Bengal and Arabian Sea. It has been observed that the RISAT-1 derived wind speed, retrieved using three Geophysical Model Functions (GMFs) viz., CMOD5.N, CMOD5 and CMOD_IFR2 are negatively biased relative to ASCAT as well as Buoy. The biases with ASCAT are 0.38 (0.98) ms−1, 0.96 (1.24) ms−1 and 1.31 (1.55) ms−1 respectively for the coastal (offshore) region. The Root Mean Square Difference (RMSD) between RISAT-1 and ASCAT are 1.58 (1.78), 1.8 (1.85) and 1.96 (1.99) in m s−1 respectively for coastal (offshore) waters, which is well within the acceptable limits. Thus it is found that the quality of the wind speed retrieved using CMOD5.N GMF is better than other GMFs. The high resolution wind data available from RISAT-1 SAR opens new pathways for assessing wind energy potential along the Indian coast.

On the prediction of storm surges during super cyclone Phailin using a deterministic ensemble Kalman filter

Deterministic ensemble Kalman filter has been used to assimilate data from the tracks of three existing altimeters in a state-of-the-art hydrodynamic model configured for the north Indian Ocean. The assimilation period has been chosen to cover the entire time history of the super cyclone Phailin occurring in the Bay of Bengal basin of the north Indian Ocean in mid-October 2013. The filter was proposed in a previous study in which the authors showed its advantage over the existing ensemble-based filter techniques. Different combinations of localization radii and inflation factors have been tried to find the optimum combination. Assimilation with this combination has been found to exhibit significant positive impact on basin-scale simulations of sea level. This conclusion has been further corroborated by comparing the surges simulated by the model with tide-gauge data acquired during the occurrence of Phailin. Assimilation has been also found to enhance the predictive capability of the model.